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ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks
Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery.
Explaining Imitation Learning through Frames
As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains.
GANExplainer: GAN-based Graph Neural Networks Explainer
With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making.
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics
Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data.
